Ok, there is a #python #DataViz issue regarding point pattern analysis my colleagues are struggling with.
It seems that #Seaborn "stretches" two-dimensional #KDE plots in a weird way with some #geopandas data, whereas running the same analysis in #QGIS with the exact same data provides good and expected results.
One option would be to go full #matplotlib to see if it is related to contour plots, but I have not gone there yet.
#matplotlib #qgis #geopandas #kde #seaborn #dataviz #python
I switched from gpxpy to #Geopandas and am now generating both a #GeoPackage and #GPX file of the combined tracks: https://github.com/lostmapper/strava-gpx-merge
Woot woot!
#FOSS4GNA accepted my proposal!
If any of y'all wanna hear about how I used #GeoPandas to replace a legacy proprietary system, you are in luck.
Anybody out there currently using #DevNet or #BruceHarris for their farmland assessments, take particular note. I can help make things easier for you!
#foss4gna #geopandas #devnet #bruceharris
Woohoo, solved complex #GIS software problem (finally) in #python and #geopandas -- took me a lot longer than usual.
Did you know, #geopandas can read and write directly to and from zipped #shapefiles, a.k.a #shizzle files (.shz). #gischat
https://geopandas.org/en/stable/docs/user_guide/io.html
#geopandas #shapefiles #shizzle #gischat
@shizamura maybe we should leard #geopandas ... (and there are other related libs mentioned on its docs) https://geopandas.org/en/stable/gallery/plotting_basemap_background.html
RT @PythonMaps@twitter.com
Roads of Europe! Shows all roads from highways (black) down to lanes (orange) and everything in between!
This map was generated using #Matplotlib #Numpy #Geopandas.
#Python #DataScience #Data #DataVisualization #Europe.
🐦🔗: https://twitter.com/PythonMaps/status/1613988903010340864
#matplotlib #numpy #geopandas #python #datascience #data #datavisualization #europe
@underdarkGIS I've been looking at a #FCC dataset about complaints to a US cable company in 2015. #Pandas #GeoPandas and #MovingPandas can hold the data. Investigating how to use distance measures to calculate trajectories. (This is a hypothetical example, no data exists about how the company dealt with the complaints and thus whether they had staff moving around the country.)
#fcc #pandas #geopandas #movingpandas
RT @JohannesHUhl1@twitter.com
Animating #map distortions: Transverse #Mercator #projection of each #UTM zone applied to the globe. Made in #python using #pyproj, #geopandas, #shapely, #matplotlib, and #PIL. #cartography #geospatial #geoviz #dataviz #gischat #gis #geopython #geovisualization #mapping
🐦🔗: https://twitter.com/JohannesHUhl1/status/1611885483252142081
#map #mercator #projection #utm #python #pyproj #geopandas #shapely #matplotlib #PIL #cartography #geospatial #geoviz #dataviz #gischat #gis #geopython #geovisualization #mapping
#FOSS #Geospatial emojis added
#ArcGIS :arcgis:
#cog :cog:
#gdal :gdal:
#GoogleEarth :gearth:
#EarthEngine :gearth_engine:
#geopandas :geopandas:
#GRASSGIS :grassgis:
#NumPy :numpy:
#osgeo :osgeo:
#OpenStreetMap :osm:
#pandas :pandas:
#PostGIS :postgis:
#Postgres :postgres:
#QGIS :qgis:
#RStats :rstat:
#SAGAGIS :saga:
#arcgis #cog #gdal #googleearth #earthengine #geopandas #grassgis #numpy #osgeo #openstreetmap #pandas #postgis #postgres #qgis #rstats #foss #geospatial #sagagis
#ArcGIS :arcgis:
#cog :cog:
#gdal :gdal:
#GoogleEarth :gearth:
#EarthEngine :gearth_engine:
#geopandas :geopandas:
#grassgis :grass:
#NumPy :numpy:
#osgeo :osgeo:
#OpenStreetMap :osm:
#pandas :pandas:
#PostGIS :postgis:
#Postgres :postgres:
#QGIS :qgis:
#RStats :rstat:
#arcgis #cog #gdal #googleearth #EarthEngine #geopandas #grassgis #numpy #osgeo #openstreetmap #pandas #postgis #postgres #qgis #rstats
#30DayMapChallenge / Music
Where do people listen to music at commuting times? Using mobile phone data, we can observe that mostly when moving in public transportation! This bubble map shows mobile phone towers scattered around the city where Spotify is accessed to.
This is part of a WIP paper where we analyze several signals to infer the mode split (share of usage per mode of transportation) in Chilean cities.
#geopandas #matplotlib #30DayMapChallenge
Population density map of #India 🇮🇳.
On the map, you can see many dark hotspots denoting densely populated cities and towns, mixed in with lighter regions with less people. All major #Indian cities show high population densities, including #NewDelhi, #Kolkata, #Chennai, #Bengaluru, #Hyderabad, and #Mumbai.
I created this map in #python using #matplotlib #geopandas #rasterio packages. For population data, I used the 2022 GHS population grid data.
#dataviz #datavisualization #30DayMapChallenge #Rasterio #geopandas #matplotlib #python #mumbai #Hyderabad #bengaluru #Chennai #kolkata #newdelhi #indian #india
#Map of all roads and streets in the #OrlandoCity, #Florida by designations.
I created this map using #Matplotlib and #Geopandas libraries in #Python. I used the road data from #OpenStreetMap, and the City of Orlando's Data Hub for city boundaries.
#30daymapchallenge #datavisualization #pythonprogramming #dataviz #mapping @matplotlib@beta.birdsite.live @matplotlib@vis.social
#mapping #dataviz #pythonprogramming #datavisualization #30DayMapChallenge #openstreetmap #python #geopandas #matplotlib #florida #orlandocity #map
#30DayMapChallenge / Movement: Change in mass transit share from 2012 to 2020 (before covid) in Santiago, Chile.
Public transport share was reduced almost eveywhere, except in areas near new metro/rail infra., as well as municipalities where we hypothesize that remote work has reduced motorized transport (thus increasing mass transit share relatively). We measured this using data fusion of mobile phone data and multiple official sources.
Made with #matplotlib and #geopandas, among other tools.
#geopandas #matplotlib #30DayMapChallenge
Just added these fancy custom #emojis to our server https://datasci.social
#pandas :python:
#rstats :rstats:
#pandas :pandas:
#numpy :numpy:
#geopandas :geopandas:
#osm :osm:
If I'm missing anything vital for #DataScience, let me know!
#emojis #pandas #rstats #numpy #geopandas #osm #datascience
We have beautiful geospatial emojis in the mapstodon.space
#ArcGIS :arcgis:
#cog :cog:
#gdal :gdal:
#GoogleEarth :gearth:
#EarthEngine :gearth_engine:
#geopandas :geopandas:
#grassgis :grass:
#NumPy :numpy:
#osgeo :osgeo:
#OpenStreetMap :osm:
#pandas :pandas:
#PostGIS :postgis:
#Postgres :postgres:
#QGIS :qgis:
#RStats :rstat:
#arcgis #cog #gdal #GoogleEarth #earthengine #geopandas #grassgis #numpy #osgeo #OpenStreetMap #pandas #postgis #postgres #qgis #RStats
Hey #Mapstodon, is there a Python tool to select the best equal-area #projection for a given region of the world?
Basically I'm looking for something like https://projectionwizard.org/ but in Python.
Or, if that does not exist, I'd be interested in any resource telling me about the criteria to switch between Albers conic, Cylindrical, Oblique Lambert azimuthal, and Transverse cylindrical equal-area projections.
I'm sure it's latitude, but more details would be good ^^
#maps #crs #gis #geopandas
#geopandas #gis #crs #maps #projection #mapstodon
Any #osmnx users here?
I encountered a weird issue today: a request with geometries_from_polygon that I had already run successfully X times and that was cached locally suddenly started returning an empty GeoDataFrame...
I also checked that the #overpass API also returns an empty result as JSON but I don't think anything changed...
Even the osmnx.geometries_from_place("Ivry-sur-Seine", tags={"buildings": True}) does that...
I've no idea what's going on :s
#maps #mapstodon #geopandas #osm #gis #overpass #osmnx